An Efficient Bandit Algorithm for Realtime Multivariate Optimization

Houssam Nassif · Yi Liu · S. V. N. Vishwanathan

108 citations0/0 files storedACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

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Abstract

Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions

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2006Statistics for experimenters: design, innovation, and discovery 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems 2001Experiments - Planning, Analysis and Parameter Design Optimization 2011An Empirical Evaluation of Thompson Sampling 2011Analysis of Thompson Sampling for the multi-armed bandit problem 2006Statistics for Experimenters: Design, Innovation, and Discovery 2012Thompson Sampling for Contextual Bandits with Linear Payoffs

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2024Beyond Preferences in AI Alignment 2017A Tutorial on Thompson Sampling 2020An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits 2020What is the Value of Experimentation and Measurement?

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